---
library_name: transformers
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: hubert-test-model
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: gtzan
      type: gtzan
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.785
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hubert-test-model

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the gtzan dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5276
- Accuracy: 0.785

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 150  | 2.0494          | 0.29     |
| No log        | 2.0   | 300  | 2.1993          | 0.19     |
| No log        | 3.0   | 450  | 1.8439          | 0.44     |
| 1.9218        | 4.0   | 600  | 1.5277          | 0.48     |
| 1.9218        | 5.0   | 750  | 1.4164          | 0.475    |
| 1.9218        | 6.0   | 900  | 1.3641          | 0.63     |
| 1.2685        | 7.0   | 1050 | 1.1557          | 0.675    |
| 1.2685        | 8.0   | 1200 | 1.0935          | 0.72     |
| 1.2685        | 9.0   | 1350 | 1.0594          | 0.71     |
| 0.7151        | 10.0  | 1500 | 1.0119          | 0.735    |
| 0.7151        | 11.0  | 1650 | 1.0868          | 0.77     |
| 0.7151        | 12.0  | 1800 | 1.3736          | 0.75     |
| 0.7151        | 13.0  | 1950 | 1.2705          | 0.77     |
| 0.4135        | 14.0  | 2100 | 1.4052          | 0.76     |
| 0.4135        | 15.0  | 2250 | 1.3864          | 0.77     |
| 0.4135        | 16.0  | 2400 | 1.4296          | 0.785    |
| 0.2311        | 17.0  | 2550 | 1.5663          | 0.77     |
| 0.2311        | 18.0  | 2700 | 1.5310          | 0.78     |
| 0.2311        | 19.0  | 2850 | 1.4884          | 0.795    |
| 0.1408        | 20.0  | 3000 | 1.5276          | 0.785    |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3